Baskakov算子线性组合的点态估计

本文利用Ditizian-Totik模给出Baskakov算子线性组合点态和整体逼近正逆定理的统一形式,得到了较高的逼近阶,并且刻画了该线性组合算子的逼近特征。     

Baskakov-Durrmeyer算子的点态逼近

本应用Ditzian-Totik模得到Baskakov-Durrmeyer算子线性组合的点态逼近的等价定理。     

Lp范数下多元正系数代数多项式倒数对非负连续函数的逼近

设d≥1为正整数,S为Rd中的单纯形,C(S)为S上连续函数类,f(x)∈C(S),f(x)≥0,f(x) 0,p＞1,‖@‖p为通常的Lp范数,‖@‖为一致范数,则存在Pn(x)∈∏+n,d={Pn(x)Pn(x)=ak≥0},常数C＞0使‖f-1/Pn‖p≤C[ω2φ(f,/4n)+‖f‖/n],这里对k,x∈Rd,k=(k1,k2,…,kd),x=(x1,x2,…,xd),记|k|=k1+k2+…+kd,|x|=x1+x2+…+xd,xk=xk11xk22…xk11dk22,ω24(f,t)为单纯形S上关于一致范数的二阶Ditzian-Totik光滑模.     

Stancu-Sikkema算子的点态估计

本文构造了Stancu—Sikkema算子,并利用点态光滑模研究了此算子的点态逼近正逆定理.     

多元正系数代数多项式倒数对非负连续函数的逼近

设d≥1为正整数,S为Rd中的单纯形,C(S)为S上的连续函数类,f(x)∈C(S),f(x)≥0,f≠0,则文中证明存在Pn(x)∈Ⅱ+n,d={Pn(x)=∑|k|≤n akxk(1-|x|)n-|k|x∈S,ak≥ 0},绝对常数C＞0使||f-1/Pn||≤C[ωψ(f,1/√n)+||f||/√n],这里k,x∈Rd,k=(k1,k2,…,kd),x=(x1,x2,…,xd),|k|=k1+k2+…+kd,|x|=x1+x2+…+xd,xk=x1k1x2k2…xdkd,ωψ(f,t)为单纯形S上的一阶Ditzian-Totik光滑模,||f||=maxx∈S|f(x)|.     

Bernstein算子线性组合同时逼近的点态估计

借助于光滑模ωψ^rλ（f,t）（0≤λ≤1）给出了Bernstein算子线性组合同时逼近的点态结果。     

一类Orlicz空间中复系数多项式的倒数逼近

定义了互余的N函数M（u）和N（v）的△条件,并研究了由这种N函数M（u）所生成的一类Orlicz空间中复系数多项式的倒数逼近问题．     

学生化Kaplan—Meier估计的正态逼近速度

设Ｆ（ｔ）是表示寿命的分布函数，Ｇ（ｔ）是表示删失的分布函数，Ｆ（ｔ）是Ｆ（ｔ）＝１－Ｆ（ｔ）的Ｋａｐｌａｎ－Ｍｅｉｅｒ估计。     

学生化Kaplan－Meier估计的正态逼近速度

设Ｆ（ｔ）是表示寿命的分布函数，Ｇ（ｔ）是表示删失的分布函数，Ｆ（ｔ）是Ｆ（ｔ）＝１—Ｆ（ｔ）的Ｋａｐｌａｎ－Ｍｅｉｅｒ估计．本文在Ｆ（ｔ），Ｇ（ｔ）均连续的条件下，证明了对任何取定的０＜ｔ＜ＴＨ，有其中ＴＨ＝ｉｎｆ｛ｔ：Ｈ（ｔ）＝０｝，Ｈ（ｔ）＝（１－Ｆ（ｔ））（１－Ｇ（ｔ）），渐近方差的刀切估计．     

关于回归函数核估计的正态逼近速度

§1.引言和主要结果 设{(X_i,Y_i);1≤i≤n}为来自二元总体的iid样本。对于回归函数r(x)=E(Y|X=x)的估计问题,早在1964年,Nadaraya首先在文献[1]中提出了如下的核估计     

On Approximation by Reciprocals of Polynomials with Positive Coefficients

In order to study the approximation by reciprocals of polynomials with real coefficients, one always assumes that the approximated function has a fixed sign on the given interval. Sometimes, the approximated function is permitted to have finite sign changes, such as $l(l\geq1)$ times. Zhou Songping has studied the case $l=1$ and $l\geq2$ in $L^{p}$ spaces in order of priority. In this paper, we studied the case $l\geq2$ in Orlicz spaces by using the function extend, modified Jackson kernel, Hardy-Littlewood maximal function, Cauchy-Schwarz inequality, and obtained the Jackson type estimation.     

Pointwise Approximation Theorems for Combinations and Derivatives of Bernstein Polynomials

We establish the pointwise approximation theorems for the combinations of Bernstein polynomials by the rth Ditzian-Totik modulus of smoothness wФ^r（f, t） where Ф is an admissible step-weight function. An equivalence relation between the derivatives of these polynomials and the smoothness of functions is also obtained.     

Interpolatory Pointwise Estimates for Polynomial Approximation

We discuss whether or not it is possible to have interpolatory pointwise estimates in the approximation of a function f∈ C[0,1] , by polynomials. For the sake of completeness, as well as in order to strengthen some existing results, we discuss briefly the situation in unconstrained approximation. Then we deal with positive and monotone constraints where we show exactly when such interpolatory estimates are achievable by proving affirmative results and by providing the necessary counterexamples in all other cases. November 16, 1998. Date revised: July 12, 1999. Date accepted: September 13, 1999.     

Pointwise Simultaneous Approximation by Combinations of Baskakov Operators

In this paper, we investigate the relation between the rate of convergence for the derivatives of the combinations of Baskakov operators and the smoothness for the derivatives of the functions approximated. We give some direct and inverse results on pointwise simultaneous approximation by the combinations of Baskakov operators. We also give a new equivalent result on pointwise approximation by these operators.     

Baskakov-Durrmeyer算子的点态逼近

本文应用Ｄｉｔｚｉａｎ－Ｔｏｔｉｋ模得到Ｂａｓｋａｋｏｖ－Ｄｕｒｒｍｅｙｅｒ算子线性组合的点态逼近的等 价定理．     

Szász-Mirakjan算子线性组合和导数的点态逼近定理

本文给出了Szász-Mirakjan算子线性组合的点态逼近定理。另外,还研究了Szász-Mirakjan算子高阶导数与所逼近函数光滑性之间的关系。     

On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights

Recently, Li [16] introduced three kinds of single-hidden layer feed-forward neural networks with optimized piecewise linear activation functions and fixed weights, and obtained the upper and lower bound estimations on the approximation accuracy of the FNNs, for continuous function defined on bounded intervals. In the present paper, we point out that there are some errors both in the definitions of the FNNs and in the proof of the upper estimations in [16]. By using new methods, we also give right approximation rate estimations of the approximation by Li’s neural networks.     

Bernstein-Sikkema算子逼近

研究Ｂｅｒｎｓｔｅｉｎ－Ｓｉｋｋｅｍａ算子的逼近问题,得到强型正定理和弱型逆定理,改进了文献［１］的结果